




import baseaa
import io
import json
import os
from copy import deepcopy

from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler







class 				lowerCAmelCase_       :






				def __init__(			self      ,snake_case__ ):
							if isinstance(snake_case__      ,snake_case__ ):
										# Don't modify user's data should they want to reuse it (e.g. in tests), because once we
										# modified it, it will not be accepted here again, since `auto` values would have been overridden
										SCREAMING_SNAKE_CASE_   : Any					  =      deepcopy(snake_case__ )
							elif os.path.exists(snake_case__ ):
										with io.open(snake_case__      ,'r'      ,encoding='utf-8' ) as f:
													SCREAMING_SNAKE_CASE_   : Dict					  =      json.load(snake_case__ )
							else:
										try:
													SCREAMING_SNAKE_CASE_   : Dict					  =      baseaa.urlsafe_baadecode(snake_case__ ).decode('utf-8' )
													SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      json.loads(snake_case__ )
										except (UnicodeDecodeError, AttributeError, ValueError):
													raise ValueError(
													    F'Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}' )

							SCREAMING_SNAKE_CASE_   : List[Any]					  =      config

							self.set_stage_and_offload()






				def 		snake_case	(			self ):
							# zero stage - this is done as early as possible, before model is created, to allow
							# ``is_deepspeed_zero3_enabled`` query and getting to the early deepspeed config object
							# during ``zero.Init()`` which needs to know the dtype, and some other hparams.
							SCREAMING_SNAKE_CASE_   : List[str]					  =      self.get_value('zero_optimization.stage'      ,-1 )

							# offload
							SCREAMING_SNAKE_CASE_   : int					  =      False
							if self.is_zeroa() or self.is_zeroa():
										SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      set(['cpu', 'nvme'] )
										SCREAMING_SNAKE_CASE_   : List[Any]					  =      set(
										    [
										        self.get_value('zero_optimization.offload_optimizer.device' ),
										        self.get_value('zero_optimization.offload_param.device' ),
										    ] )
										if len(offload_devices & offload_devices_valid ) > 0:
													SCREAMING_SNAKE_CASE_   : int					  =      True






				def 		snake_case	(			self      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : int					  =      self.config

							# find the config node of interest if it exists
							SCREAMING_SNAKE_CASE_   : Dict					  =      ds_key_long.split('.' )
							SCREAMING_SNAKE_CASE_   : Dict					  =      nodes.pop()
							for node in nodes:
										SCREAMING_SNAKE_CASE_   : Optional[int]					  =      config.get(snake_case__ )
										if config is None:
													return None, ds_key

							return config, ds_key






				def 		snake_case	(			self      ,snake_case__      ,snake_case__=None ):
							SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      self.find_config_node(snake_case__ )
							if config is None:
										return default
							return config.get(snake_case__      ,snake_case__ )






				def 		snake_case	(			self      ,snake_case__      ,snake_case__=False ):
							SCREAMING_SNAKE_CASE_   : str					  =      self.config

							# find the config node of interest if it exists
							SCREAMING_SNAKE_CASE_   : int					  =      ds_key_long.split('.' )
							for node in nodes:
										SCREAMING_SNAKE_CASE_   : Any					  =      config
										SCREAMING_SNAKE_CASE_   : str					  =      config.get(snake_case__ )
										if config is None:
													if must_exist:
																raise ValueError(F'Can\'t find {ds_key_long} entry in the config: {self.config}' )
													else:
																return

        # if found remove it
							if parent_config is not None:
										parent_config.pop(snake_case__ )






				def 		snake_case	(			self      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : str					  =      self.get_value(snake_case__ )
							return False if value is None else bool(snake_case__ )






				def 		snake_case	(			self      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : int					  =      self.get_value(snake_case__ )
							return False if value is None else not bool(snake_case__ )






				def 		snake_case	(			self ):
							return self._stage == 2






				def 		snake_case	(			self ):
							return self._stage == 3






				def 		snake_case	(			self ):
							return self._offload







class 				lowerCAmelCase_       :






				def __init__(			self      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      engine






				def 		snake_case	(			self      ,snake_case__      ,**snake_case__ ):
							# runs backpropagation and handles mixed precision
							self.engine.backward(snake_case__      ,**snake_case__ )

							# Deepspeed's `engine.step` performs the following operations:
							# - gradient accumulation check
							# - gradient clipping
							# - optimizer step
							# - zero grad
							# - checking overflow
							# - lr_scheduler step (only if engine.lr_scheduler is not None)
							self.engine.step()
							# and this plugin overrides the above calls with no-ops when Accelerate runs under
							# Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
							# training loop that works transparently under many training regimes.







class 				lowerCAmelCase_       (					lowerCamelCase_							):






				def __init__(			self      ,snake_case__ ):
							super().__init__(snake_case__      ,device_placement=snake_case__      ,scaler=snake_case__ )
							SCREAMING_SNAKE_CASE_   : Dict					  =      hasattr(self.optimizer      ,'overflow' )






				def 		snake_case	(			self      ,snake_case__=None ):
							pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed






				def 		snake_case	(			self ):
							pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed






				@property
				def 		snake_case	(			self ):
							if self.__has_overflow__:
										return self.optimizer.overflow
							return False







class 				lowerCAmelCase_       (					lowerCamelCase_							):






				def __init__(			self      ,snake_case__      ,snake_case__ ):
							super().__init__(snake_case__      ,snake_case__ )






				def 		snake_case	(			self ):
							pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed







class 				lowerCAmelCase_       :






				def __init__(			self      ,snake_case__      ,snake_case__=0.001      ,snake_case__=0      ,**snake_case__ ):
							SCREAMING_SNAKE_CASE_   : Tuple					  =      params
							SCREAMING_SNAKE_CASE_   : Dict					  =      lr
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      weight_decay
							SCREAMING_SNAKE_CASE_   : Dict					  =      kwargs







class 				lowerCAmelCase_       :






				def __init__(			self      ,snake_case__      ,snake_case__=None      ,snake_case__=0      ,**snake_case__ ):
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      optimizer
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      total_num_steps
							SCREAMING_SNAKE_CASE_   : Dict					  =      warmup_num_steps
							SCREAMING_SNAKE_CASE_   : str					  =      kwargs







from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE     :					Optional[Any]      =       logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE     :					int      =       {
    "facebook/deit-base-distilled-patch16-224": (
        "https://huggingface.co/facebook/deit-base-patch16-224/resolve/main/config.json"
    ),
    # See all DeiT models at https://huggingface.co/models?filter=deit
}

class     A__   (       snake_case__      ):







    """simple docstring"""

    __magic_name__         =						'deit'
    def __init__(			self     ,   __snake_case=7_6_8     ,   __snake_case=1_2     ,   __snake_case=1_2     ,   __snake_case=3_0_7_2     ,   __snake_case="gelu"     ,   __snake_case=0.0     ,   __snake_case=0.0     ,   __snake_case=0.02     ,   __snake_case=1E-12     ,   __snake_case=2_2_4     ,   __snake_case=1_6     ,   __snake_case=3     ,   __snake_case=True     ,   __snake_case=1_6     ,   **__snake_case     ,   ):
         super().__init__(**__snake_case       )

         snake_case											=							hidden_size
         snake_case											=							num_hidden_layers
         snake_case											=							num_attention_heads
         snake_case											=							intermediate_size
         snake_case											=							hidden_act
         snake_case											=							hidden_dropout_prob
         snake_case											=							attention_probs_dropout_prob
         snake_case											=							initializer_range
         snake_case											=							layer_norm_eps
         snake_case											=							image_size
         snake_case											=							patch_size
         snake_case											=							num_channels
         snake_case											=							qkv_bias
         snake_case											=							encoder_stride






class     A__   (       snake_case__      ):







    """simple docstring"""

    __magic_name__         =						version.parse('1.11'      )
    @property
    def 						a_     (			self       ):
         return OrderedDict(
             [
                 ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
             ]       )




    @property
    def 						a_     (			self       ):
         return 1E-4




